Common features of microRNA target prediction tools
The human genome encodes for over 1800 microRNAs, which are short noncoding RNA molecules that function to regulate gene expression post-transcriptionally. Due to the potential for one microRNA to target multiple gene transcripts, microRNAs are recognized as a major mechanism to regulate gene expres...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2014-02-01
|
| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fgene.2014.00023/full |
| _version_ | 1819094867502956544 |
|---|---|
| author | Sarah M. Peterson Sarah M. Peterson Jeffrey A. Thompson Melanie L. Ufkin Melanie L. Ufkin Pradeep eSathyanarayana Pradeep eSathyanarayana Lucy eLiaw Lucy eLiaw Clare Bates eCongdon Clare Bates eCongdon |
| author_facet | Sarah M. Peterson Sarah M. Peterson Jeffrey A. Thompson Melanie L. Ufkin Melanie L. Ufkin Pradeep eSathyanarayana Pradeep eSathyanarayana Lucy eLiaw Lucy eLiaw Clare Bates eCongdon Clare Bates eCongdon |
| author_sort | Sarah M. Peterson |
| collection | DOAJ |
| description | The human genome encodes for over 1800 microRNAs, which are short noncoding RNA molecules that function to regulate gene expression post-transcriptionally. Due to the potential for one microRNA to target multiple gene transcripts, microRNAs are recognized as a major mechanism to regulate gene expression and mRNA translation. Computational prediction of microRNA targets is a critical initial step in identifying microRNA:mRNA target interactions for experimental validation. The available tools for microRNA target prediction encompass a range of different computational approaches, from the modeling of physical interactions to the incorporation of machine learning. This review provides an overview of the major computational approaches to microRNA target prediction. Our discussion highlights three tools for their ease of use, reliance on relatively updated versions of miRBase, and range of capabilities, and these are DIANA-microT-CDS, miRanda-mirSVR, and TargetScan. In comparison across all microRNA target prediction tools, four main aspects of the microRNA:mRNA target interaction emerge as common features on which most target prediction is based: seed match, conservation, free energy, and site accessibility. This review explains these features and identifies how they are incorporated into currently available target prediction tools. MicroRNA target prediction is a dynamic field with increasing attention on development of new analysis tools. This review attempts to provide a comprehensive assessment of these tools in a manner that is accessible across disciplines. Understanding the basis of these prediction methodologies will aid in user selection of the appropriate tools and interpretation of the tool output. |
| first_indexed | 2024-12-21T23:34:14Z |
| format | Article |
| id | doaj.art-86cc5ce9d92242948399b4f110563588 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-21T23:34:14Z |
| publishDate | 2014-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-86cc5ce9d92242948399b4f1105635882022-12-21T18:46:25ZengFrontiers Media S.A.Frontiers in Genetics1664-80212014-02-01510.3389/fgene.2014.0002376191Common features of microRNA target prediction toolsSarah M. Peterson0Sarah M. Peterson1Jeffrey A. Thompson2Melanie L. Ufkin3Melanie L. Ufkin4Pradeep eSathyanarayana5Pradeep eSathyanarayana6Lucy eLiaw7Lucy eLiaw8Clare Bates eCongdon9Clare Bates eCongdon10Maine Medical Center Research InstituteUniversity of MaineUniversity of Southern MaineMaine Medical Center Research InstituteUniversity of MaineMaine Medical Center Research InstituteUniversity of MaineMaine Medical Center Research InstituteUniversity of MaineUniversity of Southern MaineUniversity of MaineThe human genome encodes for over 1800 microRNAs, which are short noncoding RNA molecules that function to regulate gene expression post-transcriptionally. Due to the potential for one microRNA to target multiple gene transcripts, microRNAs are recognized as a major mechanism to regulate gene expression and mRNA translation. Computational prediction of microRNA targets is a critical initial step in identifying microRNA:mRNA target interactions for experimental validation. The available tools for microRNA target prediction encompass a range of different computational approaches, from the modeling of physical interactions to the incorporation of machine learning. This review provides an overview of the major computational approaches to microRNA target prediction. Our discussion highlights three tools for their ease of use, reliance on relatively updated versions of miRBase, and range of capabilities, and these are DIANA-microT-CDS, miRanda-mirSVR, and TargetScan. In comparison across all microRNA target prediction tools, four main aspects of the microRNA:mRNA target interaction emerge as common features on which most target prediction is based: seed match, conservation, free energy, and site accessibility. This review explains these features and identifies how they are incorporated into currently available target prediction tools. MicroRNA target prediction is a dynamic field with increasing attention on development of new analysis tools. This review attempts to provide a comprehensive assessment of these tools in a manner that is accessible across disciplines. Understanding the basis of these prediction methodologies will aid in user selection of the appropriate tools and interpretation of the tool output.http://journal.frontiersin.org/Journal/10.3389/fgene.2014.00023/fullmachine learningmicroRNAfree energyComputational approachesconservationtarget prediction |
| spellingShingle | Sarah M. Peterson Sarah M. Peterson Jeffrey A. Thompson Melanie L. Ufkin Melanie L. Ufkin Pradeep eSathyanarayana Pradeep eSathyanarayana Lucy eLiaw Lucy eLiaw Clare Bates eCongdon Clare Bates eCongdon Common features of microRNA target prediction tools Frontiers in Genetics machine learning microRNA free energy Computational approaches conservation target prediction |
| title | Common features of microRNA target prediction tools |
| title_full | Common features of microRNA target prediction tools |
| title_fullStr | Common features of microRNA target prediction tools |
| title_full_unstemmed | Common features of microRNA target prediction tools |
| title_short | Common features of microRNA target prediction tools |
| title_sort | common features of microrna target prediction tools |
| topic | machine learning microRNA free energy Computational approaches conservation target prediction |
| url | http://journal.frontiersin.org/Journal/10.3389/fgene.2014.00023/full |
| work_keys_str_mv | AT sarahmpeterson commonfeaturesofmicrornatargetpredictiontools AT sarahmpeterson commonfeaturesofmicrornatargetpredictiontools AT jeffreyathompson commonfeaturesofmicrornatargetpredictiontools AT melanielufkin commonfeaturesofmicrornatargetpredictiontools AT melanielufkin commonfeaturesofmicrornatargetpredictiontools AT pradeepesathyanarayana commonfeaturesofmicrornatargetpredictiontools AT pradeepesathyanarayana commonfeaturesofmicrornatargetpredictiontools AT lucyeliaw commonfeaturesofmicrornatargetpredictiontools AT lucyeliaw commonfeaturesofmicrornatargetpredictiontools AT clarebatesecongdon commonfeaturesofmicrornatargetpredictiontools AT clarebatesecongdon commonfeaturesofmicrornatargetpredictiontools |